Transactions on Energy Systems and Engineering Applications 2024-02-13T19:39:12+00:00 Dr. Andres Marrugo Open Journal Systems <p><em>Transactions on Energy Systems and Engineering Applications</em> publishes peer-reviewed articles reporting on research, development, and applications on energy systems covering all areas of engineering and applied mathematics. The journal editor will enforce standards and a review policy to ensure that papers of high technical quality are accepted. The journal is published by the Universidad Tecnológica de Bolívar.</p> <p><strong>ISSN:</strong> 2745-0120 (<em>Online</em>)</p> <p><a href="" rel="license"><img src="" alt="Licencia Creative Commons" /></a></p> HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization 2024-02-13T19:39:12+00:00 Muneeb Nasir Kiara Esguerra Ibrahima Faye Tong Boon Tang Mazlaini Yahya Afidalina Tumian Eric Tatt Wei Ho <p>The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.</p> 2024-02-13T00:00:00+00:00 Copyright (c) 2024 Muneeb, Kiara, Ibrahima Faye, Tong Boon Tang, Mazlaini Yahya, Afidalina Tumian, Eric Tatt Wei Ho An Enhanced Energy Efficiency Routing for WSN based on Elephant Herding and Swarm Optimization Approaches 2023-11-20T13:23:49+00:00 Robin Abraham M. Vadivel <p>Energy utilization and inadequacy of sensor nodes are considered major drawbacks in wireless sensor networks (WSNs). This is because the sensor nodes use the battery for recharging energy. To overcome this issue WSN utilized a clustering-routing algorithm. This protocol divides the adjacent sensor nodes into separate clusters to choose a cluster head. Thus, the cluster head gathers information from all clusters and transmits it to the base station. In this article, the proposed method used cluster-based routing protocols to enhance energy efficiency and network lifetime. Moreover, this paper follows three stages to maximize energy efficiency. Initially, the clustering process is performed using dolphin swarm optimization (DSO), where a group of clusters is formed. Then the second stage is composed of cluster head selection among the group of clusters by elephant herding optimization (EHO) strategy. Finally, the collected data are necessary to forward to the base station for transferring the information. A specified path (routing) is selected by chicken swarm optimization (CSO). By using these algorithms, the network nodes support the balance of energy utilization. Experimental analysis proves when evaluated with existing methods the proposed technique has improved energy efficiency with an increase in network lifetime.</p> 2024-02-27T00:00:00+00:00 Copyright (c) 2024 Robin Abraham, M. Vadivel